Genetic variation in the IL7RA/IL7 pathway increases multiple sclerosis susceptibility
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Multiple sclerosis (MS) is characterized as an autoimmune demyelinating disease. Numerous family studies have confirmed a strong genetic component underlying its etiology. After several decades of frustrating research, the advent and application of affordable genotyping of dense SNP maps in large data sets has ushered in a new era in which rapid progress is being made in our understanding of the genetics underlying many complex traits. For MS, one of the first discoveries to emerge in this new era was the association with rs6897932[T244I] in the interleukin-7 receptor alpha chain (IL7RA) gene (Gregory et al. in Nat Genet 39(9):1083–1091, 2007; International Multiple Sclerosis Genetics Consortium in N Engl J Med 357(9):851–862, 2007; Lundmark in Nat Genet 39(9):1108–1113, 2007), a discovery that was accompanied by functional data that suggest this variant is likely to be causative rather than a surrogate proxy (Gregory et al. in Nat Genet 39(9):1083–1091, 2007). We hypothesized that variations in other genes functionally related to IL7RA might also influence MS. We investigated this hypothesis by examining genes in the extended biological pathway related to IL7RA to identify novel associations. We identified 73 genes with putative functional relationships to IL7RA and subsequently genotyped 7,865 SNPs in and around these genes using an Illumina Infinium BeadChip assay. Using 2,961 case–control data sets, two of the gene regions examined, IL7 and SOCS1, had significantly associated single-nucleotide polymorphisms (SNPs) that further replicated in an independent case–control data set (4,831 samples) with joint p values as high as 8.29 × 10−6 and 3.48 × 10−7, respectively, exceeding the threshold for experiment-wise significance. Our results also implicate two additional novel gene regions that are likely to be associated with MS: PRKCE with p values reaching 3.47 × 10−4, and BCL2 with p values reaching 4.32 × 10−4. The TYK2 gene, which also emerged in our analysis, has recently been associated with MS (Ban et al. 2009). These results help to further delineate the genetic architecture of MS and validate our pathway approach as an effective method to identify novel associations in a complex disease.
KeywordsSignificant SNPs Clinically Isolate Syndrome Wellcome Trust Case Control Consortium Quality Control Process Multiple Sclerosis Susceptibility
This work was supported by a grant from the NIH R01NS32830. JLM was partially supported by a National MS Society (NMSS) Research Award (RG 4201-A-1). The International MS Genetics Consortium is supported by R01NS049477. PLD is a Harry Weaver Neuroscience Scholar awardee of the National MS Society (NMSS); he is also a William C. Fowler scholar in Multiple Sclerosis Research and is supported by an NINDS K08 grant, NS46341. LP was a Fellow of the National MS Society, USA (FG-1665-A-1) during these studies. DAH is a Jacob Javits scholar of the NIH. This work was also supported by the Wellcome Trust (084702/Z/08/Z), the Medical Research Council (G0700061), and the Cambridge NIHR Biomedical Research Centre. We also acknowledge the use of genotype data from the British 1958 Birth Cohort DNA collection, funded by the Medical Research Council grant G0000934 and the Wellcome Trust grant 068545/Z/02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge the use of genotype data from the British 1958 Birth Cohort DNA collection, and thank the Accelerated Cure Project for its work in collecting samples from subjects with MS and for making these samples available to IMSGC investigators. We also thank the following clinicians for contributing to sample collection efforts: Accelerated Cure project (Drs. Elliot Frohman, Benjamin Greenberg, Peter Riskind, Saud Sadiq, Ben Thrower, and Tim Vollmer); Washington University (Drs. B.J. Parks and R.T. Naismith). Finally, we thank the Brigham and Women’s Hospital PhenoGenetic Project for providing DNA samples from healthy subjects that were used in the Stage 2 follow-up effort of this study. We acknowledge the work done by the Center for Genome Technology Genotyping Core within the Miami Institute for Human Genomics (University of Miami), specifically Ashley Anderson and Luis Espinosa for aid in sample processing and genotyping. We also thank the Computational Genomics Core within the Center for Human Genetics Research (Vanderbilt University), specifically Justin Giles, Yuki Bradford, and David Sexton for their support in data processing.
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